Search Results for author:
Found 9 papers, 6 papers with code
PLAN: Variance-Aware Private Mean Estimation
Under a concentration assumption on $\mathcal{D}$, we show how to exploit skew in the vector $\boldsymbol{\sigma}$, obtaining a (zero-concentrated) differentially private mean estimate with $\ell_2$ error proportional to $\|\boldsymbol{\sigma}\|_1$.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
We present an algorithm called Density Estimation from Approximate Nearest Neighbors (DEANN) where we apply Approximate Nearest Neighbor (ANN) algorithms as a black box subroutine to compute an unbiased KDE.
Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?
Given a set of points $S$ and a radius parameter $r>0$, the $r$-near neighbor ($r$-NN) problem asks for a data structure that, given any query point $q$, returns a point $p$ within distance at most $r$ from $q$.
Differentially Private Sketches for Jaccard Similarity Estimation
This paper describes two locally-differential private algorithms for releasing user vectors such that the Jaccard similarity between these vectors can be efficiently estimated.
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search
This paper reconsiders common benchmarking approaches to nearest neighbor search.
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors
We describe a novel synthetic data set that is difficult to solve for almost all existing nearest neighbor search approaches, and for which PUFFINN significantly outperform previous methods.
Data Structures and Algorithms Computational Geometry
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions
There are several variants of the similarity search problem, and one of the most relevant is the $r$-near neighbor ($r$-NN) problem: given a radius $r>0$ and a set of points $S$, construct a data structure that, for any given query point $q$, returns a point $p$ within distance at most $r$ from $q$.
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms
This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms.
